
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))


# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "004"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder


# Use environment variables to figure out path to the csvs and auto_metrics
antenna = str(int(os.environ["ANTENNA"]))
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')

antenna = "73"
csv_folder = "/home/obs/src/H6C_Notebooks/_rtp_summary_"
auto_metrics_folder = "/home/obs/src/H6C_Notebooks/auto_metrics_inspect"


display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))


import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna


# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, 'rtp_summary_table*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')

Found 16 csvs in /home/obs/src/H6C_Notebooks/_rtp_summary_
Found 16 auto_metrics notebooks in /home/obs/src/H6C_Notebooks/auto_metrics_inspect


# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0

def jd_to_summary_url(jd):
    return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'

def jd_to_auto_metrics_url(jd):
    return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'


this_antenna = None
jds = []

# parse information about antennas and nodes
for csv in csvs:
    df = pd.read_csv(csv)
    for n in range(len(df)):
        # Add this day to the antenna
        row = df.loc[n]
        if isinstance(row['Ant'], str) and '<a href' in row['Ant']:
            antnum = int(row['Ant'].split('</a>')[0].split('>')[-1]) # it's a link, extract antnum
        else:
            antnum = int(row['Ant'])
        if antnum != int(antenna):
            continue
        
        if np.issubdtype(type(row['Node']), np.integer):
            row['Node'] = str(row['Node'])
        if type(row['Node']) == str and row['Node'].isnumeric():
            row['Node'] = 'N' + ('0' if len(row['Node']) == 1 else '') + row['Node']
            
        if this_antenna is None:
            this_antenna = Antenna(row['Ant'], row['Node'])
        jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
        jds.append(jd)
        this_antenna.add_day(jd, row)
        break


# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]

df = pd.DataFrame(to_show)

# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
    df[col] = bar_cols[col]

z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
    df[col] = z_score_cols[col]

ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
    df[col] = ant_metrics_cols[col]

redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]   
for col in redcal_cols:
    df[col] = redcal_cols[col]

# style dataframe
table = df.style.hide_index()\
          .applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
          .background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
          .background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
          .background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
          .background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
          .applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
          .applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
          .applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
          .applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
          .bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
          .format({col: '{:,.4f}'.format for col in z_score_cols}) \
          .format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
          .format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
          .set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])


display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))


htmls_to_display = []
for am_html in auto_metric_htmls:
    html_to_display = ''
    # read html into a list of lines
    with open(am_html) as f:
        lines = f.readlines()
    
    # find section with this antenna's metric plots and add to html_to_display
    jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
    try:
        section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
    except ValueError:
        continue
    html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
    for line in lines[section_start_line + 1:]:
        html_to_display += line
        if '<hr' in line:
            htmls_to_display.append(html_to_display)
            break


for i, html_to_display in enumerate(htmls_to_display):
    if i == 100:
        break
    display(HTML(html_to_display))

JDs	A Priori Status	Auto Metrics Flags	Dead Fraction in Ant Metrics (Jee)	Dead Fraction in Ant Metrics (Jnn)	Crossed Fraction in Ant Metrics	Flag Fraction Before Redcal	Flagged By Redcal chi^2 Fraction	ee Shape Modified Z-Score	nn Shape Modified Z-Score	ee Power Modified Z-Score	nn Power Modified Z-Score	ee Temporal Variability Modified Z-Score	nn Temporal Variability Modified Z-Score	ee Temporal Discontinuties Modified Z-Score	nn Temporal Discontinuties Modified Z-Score	Average Dead Ant Metric (Jee)	Average Dead Ant Metric (Jnn)	Average Crossed Ant Metric	Median chi^2 Per Antenna (Jee)	Median chi^2 Per Antenna (Jnn)
2459991	RF_maintenance	0.00%	0.00%	0.00%	0.00%	-	-	-0.750627	0.925421	-1.227310	-0.842425	0.826089	-0.295898	-0.519291	-0.751360	0.6425	0.6509	0.3773	nan	nan
2459990	RF_maintenance	0.00%	0.00%	0.00%	0.00%	-	-	-0.420221	0.595297	-1.168802	-0.926584	1.265846	-0.208806	0.647288	0.239263	0.6452	0.6560	0.3775	nan	nan
2459989	RF_maintenance	0.00%	0.00%	0.00%	0.00%	-	-	-0.312876	0.596919	-1.059151	-0.561831	0.932501	-0.635694	-0.228222	-0.461598	0.6404	0.6538	0.3808	nan	nan
2459988	RF_maintenance	0.00%	0.00%	0.00%	0.00%	-	-	-0.505813	0.793032	-1.329556	-1.036920	0.905114	-0.834979	-0.587961	-0.870508	0.6336	0.6460	0.3733	nan	nan
2459987	RF_maintenance	0.00%	0.00%	0.00%	0.00%	-	-	-0.403297	0.781415	-1.306071	-0.661291	0.962514	-0.024333	1.134672	0.728598	0.6463	0.6575	0.3679	nan	nan
2459986	RF_maintenance	0.00%	0.00%	0.00%	0.00%	-	-	-0.443503	0.984033	-1.402737	-0.958965	1.305340	-0.361806	-0.409460	-1.020992	0.6567	0.6720	0.3248	nan	nan
2459985	RF_maintenance	0.00%	0.00%	0.00%	0.00%	-	-	-0.640186	0.934469	-1.311671	-0.689406	0.758830	-0.185265	0.094718	-0.161690	0.6368	0.6469	0.3728	nan	nan
2459984	RF_maintenance	0.00%	0.00%	0.00%	0.00%	-	-	-0.825907	1.078638	-1.400811	-0.321175	-0.003513	1.128740	0.207371	0.310635	0.6553	0.6616	0.3506	nan	nan
2459983	RF_maintenance	0.00%	0.00%	0.00%	0.00%	-	-	-0.670585	0.950306	-1.250596	-0.958186	0.894967	0.370354	-0.509245	-1.117912	0.6670	0.6870	0.3105	nan	nan
2459982	RF_maintenance	0.00%	0.00%	0.00%	0.00%	-	-	-0.412403	2.299877	-0.790754	-0.656843	1.026755	-0.125079	-0.383504	-0.735465	0.7199	0.7220	0.2749	nan	nan
2459981	RF_maintenance	0.00%	0.00%	0.00%	0.00%	-	-	-0.706654	0.750867	-1.444595	-1.229588	0.824593	-0.638383	-0.648612	-0.792232	0.6423	0.6533	0.3692	nan	nan
2459980	RF_maintenance	0.00%	0.00%	0.00%	0.00%	-	-	-0.407160	1.125710	-1.408788	-0.988287	0.821002	-0.321453	-1.117811	-1.227877	0.6903	0.7005	0.2970	nan	nan
2459979	RF_maintenance	0.00%	0.00%	0.00%	0.00%	-	-	-0.769262	0.783892	-1.402773	-1.050023	0.666622	0.318768	-0.626173	-0.744014	0.6337	0.6487	0.3705	nan	nan
2459978	RF_maintenance	0.00%	0.00%	0.00%	0.00%	-	-	-0.603298	0.762460	-1.492565	-1.127548	0.997386	-0.617350	-0.008194	-0.152453	0.6349	0.6487	0.3774	nan	nan
2459977	RF_maintenance	0.00%	0.00%	0.00%	0.00%	-	-	-0.584623	1.031387	-1.401126	-0.971350	1.209775	0.257985	-0.008647	-0.172322	0.6024	0.6176	0.3403	nan	nan
2459976	RF_maintenance	0.00%	0.00%	0.00%	0.00%	-	-	-0.664241	0.852667	-1.440891	-1.136943	0.933015	-0.390695	0.021181	-0.017059	0.6467	0.6595	0.3678	nan	nan

Antenna Season Report Notebook¶

Antenna 73 Report

Load relevant info from summary CSVs¶

Table 1: Per-Night RTP Summary Info For This Atenna¶

Antenna 73, Node N05:

Load antenna metric spectra and waterfalls from `auto_metrics` notebooks.¶

Figure 1: Antenna autocorrelation metric spectra and waterfalls.¶

Antenna 73: 2459991

Antenna 73: 2459990

Antenna 73: 2459989

Antenna 73: 2459988

Antenna 73: 2459987

Antenna 73: 2459986

Antenna 73: 2459985

Antenna 73: 2459984

Antenna 73: 2459983

Antenna 73: 2459982

Antenna 73: 2459981

Antenna 73: 2459980

Antenna 73: 2459979

Antenna 73: 2459978

Antenna 73: 2459977

Antenna 73: 2459976

Antenna Season Report Notebook¶

Antenna 73 Report

Load relevant info from summary CSVs¶

Table 1: Per-Night RTP Summary Info For This Atenna¶

Antenna 73, Node N05:

Load antenna metric spectra and waterfalls from auto_metrics notebooks.¶

Figure 1: Antenna autocorrelation metric spectra and waterfalls.¶

Antenna 73: 2459991

Antenna 73: 2459990

Antenna 73: 2459989

Antenna 73: 2459988

Antenna 73: 2459987

Antenna 73: 2459986

Antenna 73: 2459985

Antenna 73: 2459984

Antenna 73: 2459983

Antenna 73: 2459982

Antenna 73: 2459981

Antenna 73: 2459980

Antenna 73: 2459979

Antenna 73: 2459978

Antenna 73: 2459977

Antenna 73: 2459976

Load antenna metric spectra and waterfalls from `auto_metrics` notebooks.¶